Independent-Samples T-Test using JASP: Applications, Assumptions, and Interpretations in Research

In parametric statistical analysis, the independent-samples t-test is a widely used method for comparing the means of two different groups. Under the assumption of homogeneity of variances and normally distributed data, it evaluates the statistical significance of observed differences between group means. The fundamental ideas of the independent-samples t-test—including its assumptions, hypotheses, effect size considerations, and methodological applications in research—are investigated in this review. Furthermore, fundamental methodological issues, including the interpretation of statistical significance, are covered. Aiming to increase researchers' competency in using and interpreting the independent-samples t-test across studies, this paper provides a thorough tutorial on the test. In addition, this review highlights JASP (Jeffrey’s Amazing Statistics Program), an open-source statistical platform with an interface for conducting independent-samples t-tests. JASP provides automated assumption checks, effect size calculations, and graphical outputs, making it accessible for researchers with varying levels of statistical expertise.